Soybean Glufosinate Study
Load packages
Load dataset
Glimpse dataset
Rows: 160
Columns: 19
$ location <chr> "Lancaster", "Lancaster", "Lancaster", "Lanc…
$ year <dbl> 2019, 2019, 2019, 2019, 2019, 2019, 2019, 20…
$ herb <chr> "PRE only", "PRE only", "PRE only", "PRE onl…
$ other <chr> "PRE only", "PRE only", "PRE only", "PRE onl…
$ trade <chr> "PRE only", "PRE only", "PRE only", "PRE onl…
$ plot <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
$ trt <dbl> 1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3, 4, 4, 4,…
$ rep <dbl> 1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3,…
$ waterhempcontrol_14 <dbl> 85, 85, 95, 35, 99, 99, 99, 99, 91, 95, 85, …
$ grasscontrol_14 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
$ waterhempcontrol_28 <dbl> 88, 25, 95, 10, 95, 99, 99, 99, 98, 88, 75, …
$ grasscontrol_28 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
$ waterhempcontrol_harvest <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
$ counts_m2 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
$ biomass_gm2 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
$ yield_bu <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
$ phyto_14 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
$ dicambadrift_14 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
$ canopyclosure_14 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
Skim dataset
| Name | data |
| Number of rows | 160 |
| Number of columns | 19 |
| _______________________ | |
| Column type frequency: | |
| character | 4 |
| numeric | 15 |
| ________________________ | |
| Group variables | None |
Variable type: character
| skim_variable | n_missing | complete_rate | min | max | empty | n_unique | whitespace |
|---|---|---|---|---|---|---|---|
| location | 0 | 1 | 8 | 9 | 0 | 2 | 0 |
| herb | 0 | 1 | 8 | 46 | 0 | 10 | 0 |
| other | 0 | 1 | 8 | 46 | 0 | 10 | 0 |
| trade | 0 | 1 | 5 | 14 | 0 | 10 | 0 |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| year | 0 | 1.00 | 2019.50 | 0.50 | 2019.00 | 2019.00 | 2019.50 | 2020.00 | 2020.00 | ▇▁▁▁▇ |
| plot | 80 | 0.50 | 255.50 | 112.55 | 101.00 | 178.25 | 255.50 | 332.75 | 410.00 | ▇▇▁▇▇ |
| trt | 0 | 1.00 | 5.50 | 2.88 | 1.00 | 3.00 | 5.50 | 8.00 | 10.00 | ▇▇▇▇▇ |
| rep | 0 | 1.00 | 2.50 | 1.12 | 1.00 | 1.75 | 2.50 | 3.25 | 4.00 | ▇▇▁▇▇ |
| waterhempcontrol_14 | 0 | 1.00 | 92.71 | 12.59 | 35.00 | 92.00 | 99.00 | 99.00 | 100.00 | ▁▁▁▁▇ |
| grasscontrol_14 | 124 | 0.22 | 87.36 | 10.73 | 65.00 | 79.00 | 89.50 | 98.00 | 100.00 | ▂▃▃▃▇ |
| waterhempcontrol_28 | 0 | 1.00 | 85.47 | 19.62 | 0.00 | 80.00 | 94.00 | 99.00 | 100.00 | ▁▁▁▂▇ |
| grasscontrol_28 | 120 | 0.25 | 90.65 | 25.88 | 0.00 | 100.00 | 100.00 | 100.00 | 100.00 | ▁▁▁▁▇ |
| waterhempcontrol_harvest | 80 | 0.50 | 78.47 | 28.66 | 0.00 | 75.00 | 88.50 | 95.00 | 99.00 | ▁▁▁▂▇ |
| counts_m2 | 80 | 0.50 | 3.05 | 3.55 | 0.00 | 1.00 | 2.00 | 4.00 | 18.00 | ▇▂▁▁▁ |
| biomass_gm2 | 80 | 0.50 | 17.82 | 51.05 | 0.00 | 0.00 | 1.92 | 12.46 | 382.06 | ▇▁▁▁▁ |
| yield_bu | 52 | 0.68 | 59.21 | 11.57 | 30.44 | 49.23 | 62.93 | 68.64 | 74.80 | ▁▃▃▃▇ |
| phyto_14 | 85 | 0.47 | 1.93 | 2.81 | 0.00 | 0.22 | 1.00 | 3.00 | 12.00 | ▇▁▁▁▁ |
| dicambadrift_14 | 120 | 0.25 | 2.15 | 4.45 | 0.00 | 0.00 | 0.00 | 3.00 | 25.00 | ▇▁▁▁▁ |
| canopyclosure_14 | 120 | 0.25 | 63.38 | 12.69 | 20.00 | 60.00 | 70.00 | 70.00 | 77.00 | ▁▁▁▅▇ |
Data visualization
Waterhemp control at 14 DAT
ggplot(data, aes(x=reorder(other,waterhempcontrol_14), y=waterhempcontrol_14,
fill=trade, color=trade)) +
geom_boxplot(color="black") +
geom_jitter(alpha=0.2) +
facet_grid(year ~ location) +
coord_flip() +
labs(x="", y="Waterhemp control (%)") +
theme_minimal() +
theme(legend.position = "none")Waterhemp control at 28 DAT
ggplot(data, aes(x=reorder(other,waterhempcontrol_28), y=waterhempcontrol_28,
fill=trade, color=trade)) +
geom_boxplot(color="black") +
geom_jitter(alpha=0.2) +
facet_grid(year ~ location) +
coord_flip() +
labs(x="", y="Waterhemp control (%)") +
theme_minimal() +
theme(legend.position = "none")Waterhemp control at harvest
ggplot(data, aes(x=reorder(other,waterhempcontrol_harvest), y=waterhempcontrol_harvest,
fill=trade, color=trade)) +
geom_boxplot(color="black") +
geom_jitter(alpha=0.2) +
facet_grid(year ~ location) +
coord_flip() +
labs(x="", y="Waterhemp control (%)") +
theme_minimal() +
theme(legend.position = "none")Data wrangling
new_dt <-
data %>%
rename (herbicide = other) %>%
mutate(
wt_14 = waterhempcontrol_14/100,
wt_28 = waterhempcontrol_28/100,
yield_kg = yield_bu * 67.5) %>%
mutate(
wt_14 =
case_when(
waterhempcontrol_14 == 100 ~ 0.99,
TRUE ~ wt_14),
wt_28 = case_when(
waterhempcontrol_28 == 100 ~ 0.99,
waterhempcontrol_28 == 0 ~ 0.01,
TRUE ~ wt_28)) %>%
filter(
herbicide != "PRE only"
)Data analysis
Waterhemp control at 14 DAT
Model
- Model using herbicide trts, year, and location as fixed effects. Only rep as random effects
Anova
Analysis of Deviance Table (Type II Wald chisquare tests)
Response: wt_14
Chisq Df Pr(>Chisq)
herbicide 15.4841 8 0.05039 .
location 1.9251 1 0.16530
year 9714.1367 1 < 2e-16 ***
herbicide:location 6.4935 8 0.59213
herbicide:year 1.4430 8 0.99361
location:year 0.3624 1 0.54716
herbicide:location:year 0.6957 8 0.99954
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
- Anova shows no interaction of year, herbicide and location. There is only herbicide and year effects
New model
Model using herbicide trt as fixed, and rep, year, and location as random effects
New Anova
Analysis of Deviance Table (Type II Wald chisquare tests)
Response: wt_14
Chisq Df Pr(>Chisq)
herbicide 11.21 8 0.1901
- No effects of herbicide treatments. However, I am looking into herbicide effects.
Least square means
Compact letter display
CLD(lsmeans_14$emmeans, alpha=0.05, Letters=letters, adjust="none", reversed = TRUE) %>%
kbl() %>%
kable_classic_2(full_width = F)| herbicide | response | SE | df | lower.CL | upper.CL | .group | |
|---|---|---|---|---|---|---|---|
| 3 | PRE fb glufosinate + fomesafen + acetochlor | 0.9663473 | 0.0092718 | 131 | 0.9423191 | 0.9805724 | a |
| 9 | PRE fb glufosinate + fomesafen | 0.9644099 | 0.0096027 | 131 | 0.9396867 | 0.9792228 | a |
| 1 | PRE fb glufosinate + fomesafen + S-metolachlor | 0.9633468 | 0.0100886 | 131 | 0.9372445 | 0.9788374 | a |
| 2 | PRE fb glufosinate + pyroxasulfone | 0.9513317 | 0.0122489 | 131 | 0.9205233 | 0.9705792 | ab |
| 7 | PRE fb glufosinate + imazethapyr | 0.9491984 | 0.0129393 | 131 | 0.9165874 | 0.9694842 | ab |
| 8 | PRE fb glufosinate + acetochlor | 0.9470465 | 0.0131010 | 131 | 0.9142870 | 0.9677273 | ab |
| 6 | PRE fb glufosinate | 0.9426195 | 0.0139116 | 131 | 0.9080580 | 0.9646943 | ab |
| 4 | PRE fb glufosinate + S-metolachlor | 0.9408544 | 0.0142213 | 131 | 0.9056144 | 0.9634678 | ab |
| 5 | PRE fb glufosinate + dimethenamid-P | 0.9296513 | 0.0162142 | 131 | 0.8900147 | 0.9557144 | b |
Waterhemp control at 28 DAT
Model
- Model using herbicide trts, year, and location as fixed effects. Only rep as random effects
Anova
Analysis of Deviance Table (Type II Wald chisquare tests)
Response: wt_28
Chisq Df Pr(>Chisq)
herbicide 23.1383 10 0.010247 *
location 14.2304 3 0.002608 **
year 194.8150 1 < 2.2e-16 ***
herbicide:location 13.3607 8 0.100027
herbicide:year 2.3217 8 0.969526
location:year 0.0097 1 0.921564
herbicide:location:year 0.0139 8 1.000000
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
- Anova shows no interaction of year, herbicide and location. There is only herbicide and year effects
New model
Model using herbicide trt as fixed, and rep, year, and location as random effects
New Anova
Analysis of Deviance Table (Type II Wald chisquare tests)
Response: wt_28
Chisq Df Pr(>Chisq)
herbicide 33.38 8 5.259e-05 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
- No effects of herbicide treatments
Least square means
Compact letter display
CLD(lsmeans_28$emmeans, alpha=0.05, Letters=letters, adjust="none", reversed = TRUE) %>%
kbl() %>%
kable_classic_2(full_width = F)| herbicide | response | SE | df | lower.CL | upper.CL | .group | |
|---|---|---|---|---|---|---|---|
| 1 | PRE fb glufosinate + fomesafen | 0.9335527 | 0.0219066 | 131 | 0.8747880 | 0.9658156 | a |
| 3 | PRE fb glufosinate + fomesafen + S-metolachlor | 0.9316031 | 0.0228235 | 131 | 0.8702296 | 0.9651143 | a |
| 7 | PRE fb glufosinate + fomesafen + acetochlor | 0.9297294 | 0.0230599 | 131 | 0.8681042 | 0.9637635 | a |
| 9 | PRE fb glufosinate + acetochlor | 0.9012060 | 0.0304182 | 131 | 0.8227128 | 0.9471779 | ab |
| 8 | PRE fb glufosinate + pyroxasulfone | 0.8942952 | 0.0319956 | 131 | 0.8124210 | 0.9429429 | ab |
| 2 | PRE fb glufosinate + S-metolachlor | 0.8814474 | 0.0351589 | 131 | 0.7925916 | 0.9353419 | ab |
| 5 | PRE fb glufosinate + imazethapyr | 0.8442439 | 0.0431294 | 131 | 0.7391023 | 0.9120553 | bc |
| 6 | PRE fb glufosinate + dimethenamid-P | 0.8436929 | 0.0424061 | 131 | 0.7407439 | 0.9106904 | bc |
| 4 | PRE fb glufosinate | 0.7817276 | 0.0528757 | 131 | 0.6598743 | 0.8686175 | c |
Yield
Yield raw data distribution
library(ggridges)
new_dt %>%
ggplot(
aes(y=herbicide, x=yield_kg, fill=herbicide)) +
geom_density_ridges(scale=2, show.legend = FALSE)Data manipulation
Anova
Type III Analysis of Variance Table with Satterthwaite's method
Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
herbicide 428797 53600 8 67.094 0.5795 0.7912
siteyr 35541129 17770564 2 67.950 192.1351 <2e-16 ***
herbicide:siteyr 1318370 82398 16 67.087 0.8909 0.5820
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Least square means
Compact letter display
CLD(lsmeans_fit$emmeans, alpha=0.05, Letters=letters, adjust="none", reversed = TRUE) %>%
kbl() %>%
kable_classic_2(full_width = F)| siteyr | emmean | SE | df | lower.CL | upper.CL | .group | |
|---|---|---|---|---|---|---|---|
| 2 | Lan20 | 4672.755 | 84.84491 | 4.867462 | 4452.856 | 4892.654 | a |
| 1 | Bro19 | 4348.885 | 94.32107 | 6.946555 | 4125.503 | 4572.267 | b |
| 3 | Bro20 | 3311.142 | 84.84491 | 4.867462 | 3091.242 | 3531.041 | c |